Computers typically represent numerical quantities using a fixed number of binary digits, such that, for example, the number two and the number two billion are both represented with 32 bits of data. Fixed length schemes simplify data loads, stores, and numerical computations between computer components and systems. They also facilitate the design and implementation of data structures, encoding and decoding of fields within digital streams, and random access of elements in a collection. Unfortunately, fixed length numbers also waste valuable space during storage and transmission. Variable-length codes have been created to work around this space inefficiency. One common variable length scheme is Huffman coding, but other schemes exist, such as Arithmetic coding, Start-Step-Stop coding, etc. Huffman and Arithmetic coding both achieve optimal code lengths but are relatively slow. Start-Step-Stop coding can be set up to achieve decent code lengths on average, and can be processed in much less time, particularly on resource constrained devices. However, Start-Step-Stop coding still requires significant processing power, and is limited by having an upper bound on the number of integers that can be represented. Thus, it is with respect to these considerations and others that the subject innovations have been made.